Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor

Nuclear power plants (NPPs) are complex dynamic systems with multiple sensors and actuators. The presence of faults in the actuators and sensors can deteriorate the system's performance and cause serious safety issues. This calls for the development of fault detection and diagnosis systems for detection and isolation of such faults. In this study, fault detection and diagnosis (FDD) based on neural networks (NN) and K-nearest neighbour (KNN) algorithm is applied to a pressurized water reactor (PWR). Fault detection is first determined based on the NN. Second, the KNN algorithm is used to classify the faults. The proposed approach is capable of classifying a variety of actuator faults, sensor faults, and multiple simultaneous actuator and sensor faults. A set of simulation results is provided to demonstrate the accuracy of the FDD method. The classifier performance is further compared with other machine learning techniques.

Automated summary

This study explores fault detection and diagnosis in a pressurized water reactor using neural networks and K-nearest neighbour algorithm. The approach is effective in identifying various faults and demonstrating high accuracy in simulation results, with performance compared to other machine learning techniques.